Terrain-derived measures for basin conservation and restoration planning

Centuries of human development have altered the connectivity of rivers, adversely impacting ecosystems and the services they provide. Significant investments in natural resource projects are made annually with the goal of restoring function to degraded rivers and floodplains and protecting freshwater resources. Yet restoration projects often fall short of their objectives, in part due to the lack of systems-based strategic planning. To evaluate channel-floodplain (dis)connectivity and erosion/incision hazard at the basin scale, we calculate Specific Stream Power (SSP), an estimate of the energy of a river, using a topographically based, low-complexity hydraulic model. Other basin-wide SSP modeling approaches neglect reach-specific geometric information embedded in Digital Elevation Models. Our approach leverages this information to generate reach-specific SSP-flow curves. We extract measures from these curves that describe (dis)connected floodwater storage capacity and erosion hazard at individual design storm flood stages and demonstrate how these measures may be used to identify watershed-scale patterns in connectivity. We show proof-of-concept using 25 reaches in the Mad River watershed in central Vermont and demonstrate that the SSP results have acceptable agreement with a well-calibrated process-based model (2D Hydraulic Engineering Center's River Analysis System) across a broad range of design events. While systems-based planning of regional restoration and conservation activities has been limited, largely due to computational and human resource requirements, measures derived from low-complexity models can provide an overview of reach-scale conditions at the regional level and aid planners in identifying areas for further restoration and/or conservation assessments.     

Bayesian neural networks with physics-aware regularization for probabilistic travel time modeling

The integration of data-driven models such as neural networks for high-consequence decision making has been largely hindered by their lack of predictive power away from training data and their inability to quantify uncertainties often prevalent in engineering applications. This article presents an ensembling method with function-space regularization, which allows to integrate prior information about the function of interest, thus improving generalization performance, while enabling quantification of aleatory and epistemic uncertainties. This framework is applied to build a probabilistic ambulance travel time predictor, leveraging historical ambulance data provided by the Fire Department of New York City. Results show that the integration of a non-Gaussian likelihood and prior information from a road network analysis yields appropriate probabilistic predictions of travel times, which could be further leveraged for emergency medical service (EMS) decision making.     

Expanding the Repertoire of Low-Molecular-Weight Pentafluorosulfanyl-Substituted Scaffolds

The pentafluorosulfanyl (-SF₅) functional group is of increasing interest as a bioisostere in medicinal chemistry. A library of SF₅-containing compounds, including amide, isoxazole, and oxindole derivatives, was synthesised using a range of solution-based and solventless methods, including microwave and ball-mill techniques. The library was tested against targets including human dihydroorotate dehydrogenase (HDHODH). A subsequent focused approach led to synthesis of analogues of the clinically used disease modifying anti-rheumatic drugs (DMARDs), Teriflunomide and Leflunomide, considered for potential COVID-19 use, where SF₅ bioisostere deployment led to improved inhibition of HDHODH compared with the parent drugs. The results demonstrate the utility of the SF₅ group in medicinal chemistry.     

Tailoring solid-state synthesis routes for high confidence production of phase pure,low impedance Al-LLZO

Lithium lanthanum zirconium oxide (LLZO) garnet is a solid-state lithium ion conducting electrolyte promising all-solid-state batteries (ASSB) with high charge rates and good energy density due to its chemical stability against lithium metal anodes. LLZO has a high room temperature Li ion conductivity of ∼0.1–1 mS/cm in its cubic phase, but the stability of the cubic phase and ionic conductivity are highly sensitive to lithium stoichiometry. Stabilizing agents such as aluminum oxide and excess lithium are needed to preserve the cubic phase and compensate for lithium volatility. With the range of the end LLZO products spanning powders, porous membranes to dense membranes combined with sintering/calcination that often exceeds 1000°C, it is challenging to maintain an ideal lithium content given its high volatility from a single base powder. This study was designed to elucidate the sensitivities of aluminum doped LLZO powder synthesis and processing along its path to being utilized in a ceramic-manufacturing optimized ASSB. By utilizing thermogravimetric analysis in conjunction with in situ X-ray diffraction analysis of solid-state LLZO synthesis, it was discovered that the sensitivity of the LLZO cubic phase to lithium volatility can be reduced via early incorporation of excess lithium carbonate during initial phase formation in direct combination with controlled surface-to-volume ratios of the powders. Isostatically pressed powders of our LLZO sintered at 1100°C for 2 h showed RT ionic conductivity of 0.3–0.4 mS/cm measured via electrochemical impedance spectroscopy, and an improvement in microstructural uniformity with lowered porosity. The improved suppression of lithium volatilization has important implications for the scalable production of LLZO powders and assembly of ASSBs.     

Recent Advances in Stimuli-Responsive Smart Membranes for Nanofiltration

Nanofiltration membrane plays an increasingly important role in many industrial applications, such as water treatment and resource recovery. The performance of the smart nanofiltration membrane is largely controlled by pore size, the Donnan effect, and surface wettability, which are determined by the function of stimuli-responsive components. Smart membranes, which contain stimuli-responsive components, are capable of changing their physical and chemical properties in response to changes in the environment so that the microstructure of the membrane will have more efficient performances and broader application prospects than the current traditional nanofiltration membranes. Herein, the preparation methods of stimuli-responsive membranes are described and they are systematically classified accordingly to their mechanisms. Moreover, the latest progress of stimuli-responsive membranes in nanofiltration and the main mechanism of each stimuli-response type through relevant examples are discussed and summarized. Finally, this review provides new insights into the remaining challenges and future directions of stimuli-responsive membranes. Fueled by advances in chemistry and materials science, it is expected to build a smart and efficient nanofiltration membrane platform for the benefit of mankind.     

A Low-Temperature Batch Process for the Deposition of High-Quality Conformal Alumina Thin Films for Electronic Applications

High-quality, alumina thin films are extensively used as dielectrics, passivation layers, and barrier layers in electronics and many other applications. However, to achieve optimum stoichiometry and thus performance, the layers are often grown at elevated temperatures (>200 °C) using techniques such as atomic layer deposition (ALD). This is problematic for substrates or structures with low thermal budgets. Herein, alumina thin films are grown on 200 mm silicon substrates employing a versatile deposition method known as MVD at low deposition temperatures (35–150 °C). The chemical composition of the resulting films is investigated postdeposition using X-ray photoelectron spectroscopy (XPS) and variable angle spectroscopic ellipsometry, with fully stoichiometric Al₂O₃ achieved at deposition temperatures as low as 100 °C. Dielectric measurements confirm outstanding dielectric properties compared to typical thermal ALD layers deposited at much higher temperatures. This low-temperature deposition performance by considering the MVD reactor design and the “pump-type” regime of precursor delivery versus the “flow-type” regime of ALD is rationalized and understood. The results clearly demonstrate that alumina thin films grown with MVD are highly versatile for electronic applications and are of particular relevance and interest for the high-volume processing of dielectric, passivation, and barrier layers at low temperatures.     

Accelerating Hair Rendering by Learning High-Order Scattered Radiance

Efficiently and accurately rendering hair accounting for multiple scattering is a challenging open problem. Path tracing in hair takes long to converge while other techniques are either too approximate while still being computationally expensive or make assumptions about the scene. We present a technique to infer the higher order scattering in hair in constant time within the path tracing framework, while achieving better computational efficiency. Our method makes no assumptions about the scene and provides control over the renderer's bias & speedup. We achieve this by training a small multilayer perceptron (MLP) to learn the higher-order radiance online, while rendering progresses. We describe how to robustly train this network and thoroughly analyze our resulting renderer's characteristics. We evaluate our method on various hairstyles and lighting conditions. We also compare our method against a recent learning based & a traditional real-time hair rendering method and demonstrate better quantitative & qualitative results. Our method achieves a significant improvement in speed with respect to path tracing, achieving a run-time reduction of 40%-70% while only introducing a small amount of bias.